Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modeling Using Two New Strategies

Shakir Hussain, MA Mohamed, R Holder, A Almasri, G Shukur

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    In this article, we propose a general framework for performance evaluation of organizations and individuals over time using routinely collected performance variables or indicators. Such variables or indicators are often correlated over time, with missing observations, and often come from heavy-tailed distributions shaped by outliers. Two new double robust and model-free strategies are used for evaluation (ranking) of sampling units. Strategy 1 can handle missing data using residual maximum likelihood (RML) at stage two, while strategy two handles missing data at stage one. Strategy 2 has the advantage that overcomes the problem of multicollinearity. Strategy one requires independent indicators for the construction of the distances, where strategy two does not. Two different domain examples are used to illustrate the application of the two strategies. Example one considers performance monitoring of gynecologists and example two considers the performance of industrial firms.
    Original languageEnglish
    Pages (from-to)1966-1980
    Number of pages15
    JournalCommunications in Statistics Simulation and Computation
    Volume37
    Issue number10
    DOIs
    Publication statusPublished - 1 Jan 2008

    Keywords

    • Ranking indicators
    • Mahalanobis distance
    • Multilevel estimation
    • Performance
    • Robust statistics

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